Computer-Aided vaccine design for selected positive-sense single stranded RNA viruses

Spontaneous mutations and lack of replication fidelity in positive-sense single stranded RNA viruses (+ssRNA virus) result in emergence of genetic variants with diverse viral morphogenesis and surface proteins that affect its antigenicity. This high mutability in +ssRNA viruses has induced antiviral drug resistance and ability to overcome vaccines that subsequently resulted in rapid viral evolution and high mortality rate in human and livestock. Computer aided vaccine design and immunoinformatics play a crucial role in expediting the vaccine production protocols, antibody production and identifying suitable immunogenic regions or epitopes from the genome sequences of the pathogens. T cell and B cell epitopes can be identified in pathogens by immunoinformatics algorithms and methods that enhance the analysis of protective immunity, vaccine safety, immunity modelling and vaccine efficacy. This rapid and cost-effective computational vaccine design promotes development of potential vaccine that could induce immune response in host against rapidly mutating pathogens like +ssRNA viruses. Epitope-based vaccine is a striking concept that has been widely employed in recent years to construct vaccines targeting rapidly mutating +ssRNA viruses. Therefore, the present review provides an overview about the current progress and methodology in computeraided vaccine design for the most notable +ssRNA viruses namely Hepatitis C virus, Dengue virus, Chikungunya virus and Coronaviruses. This review also highlights the applications of various immunoinformatics tools for vaccine design and for modelling immune response against +ssRNA viruses.

In Silico Modeling and Immunoinformatics Probing Disclose the Epitope Based PeptideVaccine Against Zika Virus Envelope Glycoprotein.

Zika virus (ZIKV) is an aedes mosquito borne pathogen belonging to the member of flaviviridae subgroup is the causative agent of an emerging disease called Zika fever, known as a benign infection usually presenting as influenza like illness with cutaneous rash. Due to recent epidemic outbreaks it is realized as a major health risk which need enhanced surveillance, but no attempt has been made to design an epitope based peptide vaccine against Zika virus. Viral envelope proteins are derived from host cell membrane proteins with some viral glycoproteins and are used to cover their protective protein capsid, help the viruses to enter host cells and help them to avoid the host immune response. In this study, amino acid sequence of ZIKV envelope glycoprotein was obtained from a protein database and examined with in silico approaches to determine the most immunogenic epitopes for B cell and T cell which could induce humoral as well as cell mediated immune response. Both the linear and conformational epitopes for B cell were predicted by immunoinformatics tools housed in IEDB resources. The peptide sequence DAHAKRQTVVVLGSQEGAV from position 121 and peptide sequence from 117-137 amino acids were predicted as most potential B cell linear and conformational epitopes respectively. Epitopes for CD4+ and CD8+ T cell were also predicted by using tools within IEDB resource and peptide sequence MMLELDPPF from position 250-258 amino acids was predicted as most immunogenic CD8+ T cell epitope with immune response evoking ability prediction score (I pMHC) of 0.09139 and conservancy of 52.17%. The innate immune response for ZIKV envelope glycoprotein was determined by interferon (IFN)-gamma effectuation and mimicking capacity by immunoinformatics and molecular docking study respectively. However, this is an introductory approach to design an epitope based peptide vaccine against Zika virus; we hope this model will be very much helpful in designing and predicting novel vaccine candidate.

Inmunogenicidad de proteínas recombinantes comparada con el empleo de herramientas bioinformáticas / Immunogenicity of recombinant proteins compared with the use of bioinformatics tools

Objetivo: para determinar la utilidad de herramientas inmunoinformáticas para detectar péptidos que puedan ser inmunodominantes, y evaluar las diferencias entre las respuestas inmunes de los modelos animales empleados en los estudios preclínicos y en los humanos. Métodos: se modeló la respuesta frente a dos proteínas exógenas: la estreptocinasa recombinante y el antígeno de superficie de la hepatitis B. A partir de sus secuencias primarias se emplearon algoritmos para identificar epítopes B y T frente a moléculas HLA de clase I y II (HLA-A*0201, HLA-DRB1*0301 y HLA-DRB1*0701) y los haplotipos murinos H2-Kd y H2-Kk. Se seleccionaron los péptidos de más alta puntuación. Resultados: el algoritmo ABCPred mostró una mejor capacidad de predicción de epítopes B, mientras fue mayor la coincidencia para los programas de modelación de la respuesta T. Los epítopes generados para el haplotipo H2-Kk tuvieron una similitud mayor con los presentados por las moléculas HLA seleccionadas. Conclusiones: se presenta una metodología aplicable al desarrollo de vacunas de subunidades y multiepitópicas, así como para otros fármacos biotecnológicos de naturaleza peptídica, que permite optimizar las etapas preclínicas y clínicas, a muy bajo costo, mínimos requerimientos tecnológicos, utilización óptima de medios, recursos y capital humano disponibles en cualquier institución del sistema nacional de salud

Objective: determine the usefulness of immunoinformatics tools to detect potentially immunodominant peptides, and evaluate the differences between the immune responses provided by the animal models used in preclinical and human studies. Methods: modeling was conducted of the response to two exogenous proteins: recombinant streptokinase and hepatitis B surface antigen. Based on their primary sequences, algorithms were used to identify B and T epitopes against HLA class I and II molecules (HLA-A*0201, HLA-DRB1*0301 and HLA-DRB1*0701), and murine haplotypes H2-Kd and H2-Kk. The highest scoring peptides were chosen. Results: ABCPred algorithm showed a better prediction capacity for B epitopes, whereas coincidence was greater in modeling programs for the T response. The epitopes generated for haplotype H2-Kk had greater similitude with those presented by the HLA molecules selected. Conclusions: a methodology is presented which is applicable to the development of subunit and multiepitope vaccines, as well as other peptidic biotechnological drugs. This methodology allows optimization of the preclinical and clinical phases at a very low cost, with minimal technological requirements, optimal use of media, and resources and human capital available at any institution of the national health system

Prediction of the epitopes on HA of avian influenza virus(H5N1) / 中国免疫学杂志

Objective:To search for common epitopes of(H5N1) from different areas in the world in order to develope epitope-bacterin by the way of immunoinformatics.Methods:Firstly,homologous degree of the amino acid sequence of VIA from different areas was compared on the basis of the database in Genebank with soft-ware ClustalW1.83.The common epitopes which were able to bind strongly to MHCⅠ molecule were selected.Results:Though homologous degree of amino acid sequence of VIA from different areas was not completely similar,there were some common epitopes among these strains analyzed.The common epitopes were shown to bind strongly with MHCⅠ molecule.Conclusion:These common epitopes may be used to develope epitope-bacterin for preventing infection of AIV(H5N1).